Assessment of density in mammography

ABSTRACT

The present invention relates to a method and system that automatically classifies tissue type/patterns and density categories in mammograms. More particularly, the present invention relates to improving the quality of assessing density and tissue pattern distribution in mammography. 
     According to a first aspect, there is provided a computer-aided method of analysing mammographic images, the method comprising the steps of: receiving a mammogram; segmenting one or more anatomical regions of the mammogram; identifying a tissue type and a density category classification for an anatomical region; and using the identified tissue type and density category classifications to generate classifications output for the mammogram.

FIELD

The present invention relates to a method and system that automatically classifies tissue type/patterns and density categories in mammograms. More particularly, the present invention relates to improving the quality of assessing density and tissue pattern distribution in mammography.

BACKGROUND

Mammography is a medical imaging modality widely used for breast cancer detection. Mammography makes use of “soft” X-rays to produce detailed images of the internal structure of the human breast. These images are called mammograms and this method is considered to be the gold standard in early detection of breast abnormalities which provide a valid diagnosis of a cancer in a curable phase.

Unfortunately, the procedure of analysing mammograms is often challenging. The density and tissue type of the breasts are highly varied and in turn present a high variety of visual features due to patient genetics. These background visual patterns can obscure the often-tiny signs of malignancies which may then be easily overlooked by the human eye. Thus, the analyses of mammograms often lead to false-positive or false-negative diagnostic results which may cause missed treatment (in the case of false-negatives) as well as unwanted psychological and sub-optimal downstream diagnostic and treatment consequences (in the case of false-positives).

Most developed countries maintain a population-wide screening program, a comprehensive system for calling in women of a certain age group, free of symptoms to have regular breast screening. These screening programs require highly standardized protocols to be followed by experienced specialist trained doctors who can reliably analyse a large number of mammograms routinely. Most professional guidelines strongly suggest reading of each mammogram by two equally expert radiologists (industrially known as double-reading). Nowadays, with the number of available highly skilled radiologists scarce and decreasing, the double-reading requirement is often impractical or impossible.

When analysing mammograms, the reliable identification of anatomical structures is important for visual evaluation and especially for analytic assessment of visual features based on their anatomic location and their relation to anatomic structures, which may have profound implications on the final diagnostic results. In the case that anatomic structures appear distorted, they may also indicate the presence of possible malignancies.

The analysis of mammograms is further complicated by visual confounding, obstructing, and/or occluding factors such as breast density or the type of structural pattern of the breast tissue. Both breast density and breast tissue type contributes to producing mammograms that are difficult to screen for cancer.

Uniformity of classification, by having a method of descriptively assigning objects into a predefined set of categories, reduces vague communication amongst professionals and ensures improved monitoring of breast cancer in mammography.

There are different types of patterns in breast tissue that affect the detectability of breast cancers. Thus, it is important for quality control reasons to know what kind of pattern is present. There are five mammography parenchymal patterns known as “Tabar patterns”, named after professor Laszlo Tabar who developed this classification.

The Tabar patterns (or classifications) are based on a histologic-mammographic correlation with a three-dimensional, subgross (thick-slice) technique, and on the relative proportion of four “building blocks” (nodular densities, linear densities, homogeneous fibrous tissue, radiolucent fat tissue). The five classifications are as follows:

-   -   1. Balanced proportion of all components of breast tissue with a         slight predominance of fibrous tissue     -   2. Predominance of fat tissue     -   3. Predominance of fat tissue with retroareolar residual fibrous         tissue     -   4. Predominantly nodular densities     -   5. Predominantly fibrous tissue (dense breast)

Classes 4 and 5 are considered high risk, meaning that it is difficult to detect cancers in the breast with those patterns, whereas classes 1, 2 and 3 are considered lower risk as it is easier to spot cancerous regions.

Some therapies may alter the pattern by increasing parenchymal density, as in hormone replacement therapy (HRT), or reducing it as in therapies with selective oestrogen-receptor modulators (SERM).

Similarly, breast density categories are classified by radiologists using the BI-RADS system. Again, this classification is used for quality control purposes. For example, it is very difficult to spot an anomaly in dense breasts. There are four categories in the BI-RADS system:

-   -   A. The breasts are almost entirely fatty     -   B. There are scattered areas of fibroglandular density     -   C. The breasts are heterogeneously dense, which may obscure         small masses     -   D. The breasts are extremely dense, which lowers the sensitivity         of mammography

Importantly, breast densities and tissue patterns are also known to have a mutual correlation to breast cancer development.

The complexity of assessing the images may cause difficulty when it comes to detecting abnormalities or diseases accurately and precisely, thus affecting the determination of diagnosis and therapeutic preparation. Therefore, meaningful medical image data analysis plays an important role in stages of cancer identification and treatment, as well as in further research.

However, the commonly used method of manual classification may lead to disagreement and classification error, which can prove crucial in medical treatment.

The present invention provides a novel method and system that combines quantitative estimation of density and qualitative estivation of tissue patterns using deep learning to aid the assessment of confounding factors in mammography.

SUMMARY OF THE INVENTION

Aspects and/or embodiments seek to provide a method of automatically classifying the tissue type and breast density of mammograms. Aspects and/or embodiments also seek to address the problems relating to human inaccuracy and highly skilled human resources.

According to a first aspect, there is provided a computer-aided method of analysing mammographic images, the method comprising the steps of: receiving a mammogram; segmenting one or more anatomical regions of the mammogram; identifying a tissue type and a density category classification for an anatomical region; and using the identified tissue type and density category classifications to generate classifications output for the mammogram.

In this way, classification of a mammogram can be automatically performed and thereby reducing human error and need for additional highly skilled medical professionals. It can also improve the analysis of mammographic image with the possibility of jointly estimating the tissue patterns and density category which generates better overall classification results.

The term segmentation does not merely represent dividing an image into one or more parts, for example, by using a bounding box around a particular region or identifying a central location of a particular region. Instead, the segmentation provided by this method determines a number of useful characteristic data, such as area, shape and size, which is more precise than traditional methods. As a result, this segmentation method can be used to more accurate indicate a malignant tumour. In addition, the method disclosed herein is operable to segment a region without any prior input other than an image and requires no feature engineering steps.

Optionally, the step of calibration of intensity levels based on standard anatomical structures is provided. The calibration of intensity levels can more efficiently identify regions of interest in a mammography.

In one embodiment, the step of segmenting one or more anatomical regions comprises determining a primary anatomical region of the mammogram. Optionally the primary anatomical region comprises a region where parenchyma is present. This can be considered to be a region of interest.

Optionally, this embodiment further comprises the steps of selecting one or more sample areas of the primary anatomical region; and identifying the tissue type and the density category classification in dependence upon the sample area.

Selecting a sample area to represent the region of interest can decrease the computational power needed by the system to correctly classify the tissue type and density category. The size of the sample can be varied in accordance with the input parameters of the convolution network.

In another embodiment, the step of segmenting one or more anatomical regions comprises determining a secondary anatomical region of the mammogram. Optionally, the secondary anatomical region comprises a region where parenchyma is not present.

Optionally, in this embodiment, the step of identifying the tissue type and the density category classification is in dependence upon the secondary anatomical region.

Optionally, the step of identifying the tissue type and the density category is performed in one or more convolutional neural networks, CNN. Optionally, the one or more CNN is trained to classify the tissue type and density category. Optionally, the at least one CNN is trained independently to classify tissue type or density category.

In this way, the identification of the tissue type and density category is performed automatically using well trained convolution networks. Convolution networks can be used regardless of whether a parenchyma is detected or not.

Optionally, the step of segmentation is carried out in a fully convolution network, FCN. In this way, the anatomical segmentation is also automatically performed.

Optionally, the classification output for the mammogram comprises a joint tissue type and density category classification. Providing a joint estimate of tissue pattern and density can produce more accurate and discriminative analyses of mammographic images.

Optionally, the step of receiving the mammogram comprises the use of one or more digital imaging and communications in medicine, DICOM, files.

Optionally, the one or more anatomical region comprises the entire breast area.

Optimally, the one or more sample area is selected using uniform or Poisson sampling.

Optionally, the one or more sample area is selected in dependence upon the input of the one or more CNN.

According to a second aspect, there is provided an apparatus to perform the method of any preceding claim.

According to third aspect, there is provided a system operable to perform the method of any preceding claim.

According to a fourth aspect, there is provided a computer program product operable to perform the aforementioned method.

According a fifth aspect, there is a method for training one or more neural networks to classify a mammogram, the method comprises, receiving a mammogram, segmenting one or more anatomical regions of the mammogram, identifying a tissue type and a density category classification for an anatomical region and using the identified tissue type and density category classifications to train the one or more neural networks.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments will now be described, by way of example only and with reference to the accompanying drawings having like-reference numerals, in which:

FIG. 1 illustrates a flowchart showing the method of the present invention.

FIG. 1a illustrates a flowchart wherein the convolution networks are trained to predict the tissue type/pattern of the mammogram independently.

FIG. 1b illustrates a flowchart wherein the convolution networks are trained to predict the density of the mammogram independently.

FIG. 2 illustrates a flowchart of how anatomical segmentation is used for the present invention.

SPECIFIC DESCRIPTION

FIG. 1 depicts an example embodiment of the present invention. Where appropriate, the same reference numerals have been used in FIGS. 1a and 2. The invention will now be described using FIGS. 1-2.

As seen in FIG. 1, having performed a medical scan of a patient (mammography), the scanned images are collated in DICOM format 101, which is a file format commonly used to store medical images. The method uses pre-processed data that is stored on a Picture Archiving Communication Systems (PACS) that radiology departments use in hospitals. The output of this method also enriches the PACS database to improve future applications of analysing mammographic images.

As previously mentioned, an important aspect of analysing mammographic images is anatomically segmenting the mammographic images 103, 202.

As an example, FIG. 2 depicts the use of anatomically segmenting the mammographic images using fully convolution networks, FCN. In this example, the image data is captured from the DICOM file and may require some form of pre-processing before being used by the FCN 201. This may include various techniques such as window levelling, etc.

However, for the purposes of this embodiment, any method of anatomical segmentation can be used.

After the anatomical regions have been segmented, the system identifies whether or not parenchyma is detected in the breast. Parenchyma may also be considered as an anatomical region. As illustrated in FIG. 2, when no parenchyma is detected 103 c, the system automatically classifies the breast with tissue type class 2 (Tabar) and a breast density category A (BI-RADS).

On the other hand, if parenchyma is detected 103 b then the system follows a different approach. As depicted in FIGS. 1 and 2, when parenchyma is found in the image, a patch is sampled from the parenchyma region 103 a. The patch can be sampled using uniform sampling or Poisson sampling, and the size of the patch can be varied.

Once acquired, the sample patch is fed into a convolutional neural network (CNN) 104 which is trained to classify the patch under one of the remaining four tissue types (Tabar) and one of the remaining three density categories (BI-RADS).

In some embodiments, each CNN node may have been trained to focus on a specific aspect of breast density or tissue type/pattern, or an ensemble of different CNN nodes and/or models may be used in combination to classify the sample patches and/or mammographic images. As an example, FIG. 1a depicts two CNN models 104 a that are independently trained to predict tissue type/pattern and breast density.

This embodiment utilises deep learning techniques to train and develop the convolution network. A large database of full-field digital mammograms can be used to train the system to correctly classify the mammogram.

With the CNN having classified the tissue type and breast density of the sample patch, the CNN can now predict and output the tissue type and density classifications of the full mammographic image 105. As seen in FIG. 1, the full mammographic image 102 is fed into the image pathway of the CNN. In another embodiment, the sample patch and full mammographic image are fed through different CNN models before a joint prediction layer obtains a tissue type and density category classification.

The joint estimation of tissue type and breast density yields a more accurate and discriminative categorisation of mammograms and therefore even lead to better diagnostic planning and improved patient care. The present invention therefore improves the quality control analysis of mammography readings.

Machine learning is the field of study where a computer or computers learn to perform classes of tasks using the feedback generated from the experience or data gathered that the machine learning process acquires during computer performance of those tasks.

Typically, machine learning can be broadly classed as supervised and unsupervised approaches, although there are particular approaches such as reinforcement learning and semi-supervised learning which have special rules, techniques and/or approaches. Supervised machine learning is concerned with a computer learning one or more rules or functions to map between example inputs and desired outputs as predetermined by an operator or programmer, usually where a data set containing the inputs is labelled.

Unsupervised learning is concerned with determining a structure for input data, for example when performing pattern recognition, and typically uses unlabelled data sets. Reinforcement learning is concerned with enabling a computer or computers to interact with a dynamic environment, for example when playing a game or driving a vehicle.

Various hybrids of these categories are possible, such as “semi-supervised” machine learning where a training data set has only been partially labelled. For unsupervised machine learning, there is a range of possible applications such as, for example, the application of computer vision techniques to image processing or video enhancement. Unsupervised machine learning is typically applied to solve problems where an unknown data structure might be present in the data. As the data is unlabelled, the machine learning process is required to operate to identify implicit relationships between the data for example by deriving a clustering metric based on internally derived information. For example, an unsupervised learning technique can be used to reduce the dimensionality of a data set and attempt to identify and model relationships between clusters in the data set, and can for example generate measures of cluster membership or identify hubs or nodes in or between clusters (for example using a technique referred to as weighted correlation network analysis, which can be applied to high-dimensional data sets, or using k-means clustering to cluster data by a measure of the Euclidean distance between each datum).

Semi-supervised learning is typically applied to solve problems where there is a partially labelled data set, for example where only a subset of the data is labelled. Semi-supervised machine learning makes use of externally provided labels and objective functions as well as any implicit data relationships. When initially configuring a machine learning system, particularly when using a supervised machine learning approach, the machine learning algorithm can be provided with some training data or a set of training examples, in which each example is typically a pair of an input signal/vector and a desired output value, label (or classification) or signal. The machine learning algorithm analyses the training data and produces a generalised function that can be used with unseen data sets to produce desired output values or signals for the unseen input vectors/signals. The user needs to decide what type of data is to be used as the training data, and to prepare a representative real-world set of data. The user must however take care to ensure that the training data contains enough information to accurately predict desired output values without providing too many features (which can result in too many dimensions being considered by the machine learning process during training, and could also mean that the machine learning process does not converge to good solutions for all or specific examples). The user must also determine the desired structure of the learned or generalised function, for example whether to use support vector machines or decision trees.

The use of unsupervised or semi-supervised machine learning approaches are sometimes used when labelled data is not readily available, or where the system generates new labelled data from unknown data given some initial seed labels.

Machine learning may be performed through the use of one or more of: a non-linear hierarchical algorithm; neural network; convolutional neural network; recurrent neural network; long short-term memory network; multi-dimensional convolutional network; a memory network; fully convolutional network or a gated recurrent network allows a flexible approach when generating the predicted block of visual data. The use of an algorithm with a memory unit such as a long short-term memory network (LSTM), a memory network or a gated recurrent network can keep the state of the predicted blocks from motion compensation processes performed on the same original input frame. The use of these networks can improve computational efficiency and also improve temporal consistency in the motion compensation process across a number of frames, as the algorithm maintains some sort of state or memory of the changes in motion. This can additionally result in a reduction of error rates.

Developing a machine learning system typically consists of two stages: (1) training and (2) production. During the training the parameters of the machine learning model are iteratively changed to optimise a particular learning objective, known as the objective function or the loss. Once the model is trained, it can be used in production, where the model takes in an input and produces an output using the trained parameters.

Any system feature as described herein may also be provided as a method feature, and vice versa. As used herein, means plus function features may be expressed alternatively in terms of their corresponding structure.

Any feature in one aspect of the invention may be applied to other aspects of the invention, in any appropriate combination. In particular, method aspects may be applied to system aspects, and vice versa. Furthermore, any, some and/or all features in one aspect can be applied to any, some and/or all features in any other aspect, in any appropriate combination.

It should also be appreciated that particular combinations of the various features described and defined in any aspects of the invention can be implemented and/or supplied and/or used independently. 

1. A computer-aided method of analysing mammographic images, the method comprising: receiving a mammographic image; segmenting one or more anatomical regions of the mammographic image; identifying a tissue type and a density category classification for an anatomical region; and using the identified tissue type and density category classifications to generate classifications output for the mammographic image.
 2. The method of claim 1 further comprising calibration of intensity levels based on standard anatomical structures.
 3. The method of claim 1, wherein segmenting one or more anatomical regions comprises one or both of: determining a primary anatomical region of the mammographic image; and/or determining a secondary anatomical region of the mammographic image.
 4. The method of claim 3, wherein the primary anatomical region comprises a region where parenchyma is present.
 5. The method of claim 3 further comprising: selecting one or more sample areas of the primary anatomical region; and identifying the tissue type and the density category classification in dependence upon the sample area.
 6. (canceled)
 7. The method of claim 3, wherein the secondary anatomical region comprises a region where parenchyma is not present.
 8. The method of claim 3 further comprising identifying the tissue type and the density category classification in dependence upon the secondary anatomical region.
 9. The method of claim 5, wherein identifying the tissue type and the density category is performed in one or more convolutional neural networks (CNN).
 10. The method of claim 9 wherein the one or more CNN is trained to classify the tissue type and density category.
 11. The method of claim 10 wherein the one or more CNN is independently trained to classify tissue type or density category.
 12. Then method of claim 10 wherein both the one or more sample areas and full mammographic image are provided as an input for the one or more CNN.
 13. The method of claim 1 wherein segmenting the one or more anatomical regions of the mammographic image is carried out in a fully convolution network, FCN.
 14. The method of claim 1 wherein the classification output for the mammographic image comprises a joint tissue type and density category classification.
 15. The method of claim 1 wherein receiving the mammographic image comprises the use of one or more digital imaging and communications in medicine (DICOM) files.
 16. The method of claim 1 wherein the one or more anatomical region comprises the entire breast area.
 17. The method of claim 5 wherein the one or more sample areas is selected using uniform or Poisson sampling.
 18. The method of claim 12 wherein the one or more sample areas is selected in dependence upon the input of the one or more CNN.
 19. An apparatus operable to perform the method of claim
 1. 20. (canceled)
 21. A method for training one or more neural networks to classify a mammogram, the method comprises: receiving a mammographic image; segmenting one or more anatomical regions of the mammographic image; identifying a tissue type and a density category classification for an anatomical region; and using the identified tissue type and density category classifications to train the one or more neural networks.
 22. A computer program product including one or more non-transitory machine readable mediums encoded with instructions that when executed by one or more processors cause a process to be carried out for analysing mammographic images, the process comprising receiving a mammographic image; segmenting one or more anatomical regions of the mammographic image; identifying a tissue type and a density category classification for an anatomical region; and using the identified tissue type and density category classifications to generate classifications output for the mammographic image. 